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Classification management for grassland using MODIS data: a case study in the Gannan region, China Xia Cui
a b
a
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, Zheng Gang Guo , Tian Gang Liang , Yu Ying Shen a
, Xing Yuan Liu & Yong Liu
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b
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State Key Laboratory of Grassland Agro-ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou City, 730020, PR China b
College of Earth and Environmental Science, Lanzhou University, Lanzhou City, 730000, PR China Available online: 11 Nov 2011
To cite this article: Xia Cui, Zheng Gang Guo, Tian Gang Liang, Yu Ying Shen, Xing Yuan Liu & Yong Liu (2012): Classification management for grassland using MODIS data: a case study in the Gannan region, China, International Journal of Remote Sensing, 33:10, 3156-3175 To link to this article: http://dx.doi.org/10.1080/01431161.2011.634861
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International Journal of Remote Sensing Vol. 33, No. 10, 20 May 2012, 3156–3175
Classification management for grassland using MODIS data: a case study in the Gannan region, China XIA CUI†‡, ZHENG GANG GUO*†, TIAN GANG LIANG†, YU YING SHEN†, XING YUAN LIU† and YONG LIU‡
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†State Key Laboratory of Grassland Agro-ecosystems, College of Pastoral Agriculture Science and Technology, Lanzhou University, Lanzhou City 730020, PR China ‡College of Earth and Environmental Science, Lanzhou University, Lanzhou City 730000, PR China (Received 26 July 2010; in final form 15 August 2011) Classification of grasslands is a convenient method to measure and manage the sustainability of Chinese grasslands. In this study, a timely and reliable procedure was examined using remote-sensing (RS) techniques. Linear regression analysis between field survey data and Moderate-Resolution Imaging Spectroradiometer (MODIS) data showed that among 17 vegetation indices (VIs) evaluated, the enhanced vegetation index (EVI) was the best VI to simulate forage dry biomass and cover in the Gannan region. The results of precision estimation of the models showed that power and logarithm regression satisfactorily simulated grassland dry biomass and grassland cover, respectively. The index of classification management of grasslands (ICGs) was used to subdivide grasslands into conservation grasslands and moderately productive grasslands in the Gannan region, where no grasslands fell into intensively productive grasslands. Conservation grasslands accounted for 2.04% of the available grasslands, whereas moderately productive grasslands were 97.96% of the available grasslands, and this is related to the history of the grasslands’ use and the per capita income in the Gannan region. This study proposes that the area of conservation grasslands and that of moderately productive grasslands are determined by increases in per capita income and changes in the human use of grasslands.
1.
Introduction
Grasslands are the most widely existing land cover type in China and play significant roles in animal production and other ecological functions (Guo et al. 2003). In the past 50 years, vast areas of grassland have been reclaimed for arable land crops (e.g. wheat, corn and tomato) (Yue and Wei 2009) in order to accommodate the increasing human population and social economic development (Nan 2005). Farmers have focused on maximizing economic benefits to meet the increasing demands for meat (Xu 1988). As a result of these economic and social pressures, the grassland areas in China have decreased from 392 × 106 ha in 1950 to 327 × 106 ha in 1990 and to 262.7 × 106 ha in 2004 (Wang and Han 2005, Shi et al. 2006). Decreases in grassland areas and maximization of animal production led to grassland overgrazing and neglect of the ecological service functions provided by grasslands (Guo et al. 2003). *Corresponding author. Email:
[email protected] International Journal of Remote Sensing ISSN 0143-1161 print/ISSN 1366-5901 online © 2012 Taylor & Francis http://www.tandf.co.uk/journals http://dx.doi.org/10.1080/01431161.2011.634861
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Such use and management of grasslands in China has led to degradation similar to that observed in other parts of the world (Baumer 1982, Bragg and Tallis 2001, Carvalho and Batello 2009). Grassland degradation causes a decrease in livestock carrying capacity per unit, increases salinization, desertification and soil erosion and encourages sand dune activity (Guo et al. 2006). To protect grasslands from further degradation and promote grassland restoration, a reduction in or cessation of grazing has been suggested (Smith et al. 1995), and carried out by the Chinese government by reducing the amount of livestock on the severely degraded grasslands and water catchment regions since 2002 (Nie et al. 2008). However, these policies remain very unpopular in China (Guo et al. 2004, 2006), as has been the case with similar approaches used in the past in Arizona and New Mexico (Voorthuizen 1978). These measures are unpopular because reducing the amount of livestock directly decreases the income of the farmers. To combat this negative outcome of these policies while simultaneously protecting the ecology of China’s grasslands, classification management of grassland proposed by Guo et al. (2003) has been gradually introduced into Chinese grassland management protocols (Li et al. 2008, Ren et al. 2008) and is considered a sustainable approach for the use of grassland resources in China (Wang et al. 2006, Li et al. 2008, Ren et al. 2008, Wang and Ba 2008). This approach, based on the carrying capacity and the value of ecological services provided by various grassland types, uses an index of classification management of grassland (ICG) and classifies the grasslands into intensively productive grasslands, moderately productive grasslands and conservation grasslands. ICG utilizes a spatially explicit approach to assess the relationship between livestock production and ecological conservation (Guo et al. 2003). Classification management is an administrative approach to managing grassland ecosystems. However, there are some shortcomings in this approach. First, information derived exclusively from field surveys is inefficient and costly due to the magnitude of the grassland area; second, there is poor accessibility to these areas (Asrar et al. 1986); finally, it is difficult to determine the division of transition grasslands between the three types because this approach neglects the spatial difference in each grassland type. In order to improve grassland classification and management these shortcomings need to be addressed and the classification approach for grasslands needs to be improved. Grasslands in the Gannan region of China play a significant role in providing important ecosystem-regulating services such as reducing erosion by supporting slope stability, water regime regulation, purifying water from fertilizers and pesticides and supporting biodiversity and cultural services (Xie et al. 2001, Guo et al. 2006). They not only provide forage to feed livestock but also play a critical role in alleviating many environmental and ecological problems that local communities face (Guo et al. 2004). In the past decades, farmers in this region have largely focused on increasing livestock numbers to maximize economic benefit, resulting in the degradation of 90% of grasslands in this region. This represents a landscape change from one that was composed of 50% moderately degraded grasslands in the 1980s to a landscape currently dominated by degraded grasslands (An et al. 2007). Protocols such as anti-grazing and fencing have been implemented since 2003 in order to improve grassland management; however, these measures have not achieved the anticipated target for grassland restoration (Wang et al. 2006). Some of the grasslands within the Gannan region are still in a state of degradation in large part due to the continued use of the land for animal production, which is the most viable economic income (An et al. 2007).
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Remote-sensing (RS) techniques are widely used to monitor accurately and timely the temporary and spatial changes in grasslands by providing useful information to farmers to enable them to change their management patterns according to changes in grassland conditions (Seaquist et al. 2003, Xu et al. 2007, Jesús et al. 2009). A geographic information system (GIS) provides a geolocated spatial distribution of grasslands by combining field survey data and NASA-acquired daily Moderate-Resolution Imaging Spectroradiometer (MODIS) data (250–1000 m spatial resolution) (Shree et al. 2000). The objective of this study is to improve grassland classification for management, integrating RS, GIS and field observations to manage Gannan grasslands more efficiently.
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2. Materials and methods 2.1 Study area The Gannan region is located on the northeast of the Qinghai–Tibet Plateau, between 33◦ 06 –35◦ 44 N and 100◦ 46 –104◦ 44 E (figure 1), with a 272.26 × 104 ha grassland area, of which 93.9% is available grasslands (Zhang et al. 2008). Gannan has a continental plateau climate, with a mean elevation of more than 3000 m (Yue and Wei 2009). Annual precipitation varies from 400 to 800 mm, with a potential evaporation of 1200–1350 mm. Mean annual temperatures range from 1◦ C to 3◦ C, with maximum temperatures of 28.9◦ C occurring in August, and minimum temperatures of –30.6◦ C in January (Yang et al. 2007). It has only 2 months to accumulate temperatures above 10◦ C, and the annual mean sunshine time is 2000–2400 hours (Guo et al. 2004). Grasslands in the Gannan region play an important role in animal production, with livestock production being the main economic income for local pastoralists (Guo et al. 2004). The Gannan grasslands have vast genetic resources of flora and have an impact on water conservation and soil erosion. 2.2 Collecting data of dry biomass and cover by field survey According to the grassland type and terrain conditions in the Gannan region, 228 plots with sizes of 10 m × 10 m were selected (figure 1). Field survey plots were chosen, ensuring a 2.5 km horizontal distance between plots and homogeneity in both vegetation and land use, and the geographic location of the plots was selected to ensure that similar grassland types and geomorphology exist within a 500 m range around the plot, considering that MODIS pixels are 500 m × 500 m. In 2005, each plot was fenced to exclude grazing. From 2006 to 2009, four subplots of 1 × 1 m were taken diagonally across the fenced plot to collect quantitative information of the vegetation. A total of 912 subplots were collected over all 228 sites. The latitude and longitude of each subplot were recorded using GPS (eTrex vista c, Olathe, KS, USA). In each subplot, total forage dry biomass was determined by cutting the herbage to ground level using shears and removing all litter and dry material from clipped plants before measuring the grassland biomass. The biomass samples were oven-dried at 80◦ C for 24–48 hours or until constant dry biomass was recorded. Grassland cover was measured using 100 points by vertical forage projection for each subplot expressed as a percentage. The average dry forage biomass and cover per plot were calculated with the four subplot measurements, and statistical analyses were conducted between vegetation indices (VIs) and grassland biomass and cover.
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Location of the plots in the Gannan region.
2.3 MODIS data acquisition and preprocessing The daily data (tile h26v05) of the MOD09GA product (MODIS Terra/Aqua Surface Reflectance Daily L2G Global 500 m and 1 km SIN Grid V005) in the growing seasons of 2001–2009 were acquired from the Earth Observing System (EOS) Data Gateway (https://wist.echo.nasa.gov/), covering a large area (33◦ –36◦ E, 100◦ –105◦ N). From this, 500 m spatial resolution data were chosen, and seven bands of these data were corrected to the top of the atmosphere for the effect of gaseous absorption, molecules and aerosol scattering (Vermote and Vermeulen 1999, Houborg et al. 2007). In this study, seven bands with 620–670 nm (band 1), 841–876 nm (band 2), 459–479 nm (band 3), 545–565 nm (band 4), 1230–1250 nm (band 5), 1628–1652 nm (band 6) and 2105–2155 nm (band 7) at 500 m resolution were used to calculate 17 VIs (table 1). Surface reflectances of MODIS bands 1–7 (hereafter referred to as MODIS data) acquired in our study area were converted to Albers equal-area conformal projection and were clipped to the study area in ArcGIS9.1 (Environmental Systems Research Institute Inc. Redlands, CA, USA, 2005). Pixel values of MODIS data corresponding to each subplot according to the latitude and longitude recorded in the field were extracted and converted into a text file for calculating VIs. 2.4 Vegetation indices Many different VI algorithms are used to monitor the Earth’s vegetative cover and biomass corresponding to the characteristics of different regions. Ground-based subplot data and RS data were related spatially through plot coordinates at a pixel level
2 (NIR – R)/(NIR + R + 0.16) SWIR2 SWIR1 − R (1 + L) − SWIR1 + R + L 2 NIR/R NIR/G GEMI = η(1 − 0.25η) − (R − 0.125)/(1 − R)
NIR − R ×2.5 NIR + C1 R − C2 B + L (NIR – R)/(NIR + R) (NIR – G)/(NIR + G) (NIR – B)/(NIR + B) (NIR – (G + R))/(NIR + (G + R)) (NIR – (G + B))/(NIR + (G + B)) (NIR – (R + B))/(NIR + (R + B)) (NIR – (G + R + B))/(NIR + (G + R + B)) (NIR − R)/(NIR + R) + 0.5 NIR/(NIR + R) NIR − R (1 + L) NIR + R + L 2NIR + 1 − (2NIR + 1)2 − 8(NIR − R)
Formula
Jordan (1969) Xue et al. (2007) Pinty and Verstraete (1992)
Marsett et al. (2006)
Qi et al. (1994) Steven (1998)
Huete (1988)
Tucker et al. (1986) Wang et al. (2007) Wang et al. (2007) Wang et al. (2007) Wang et al. (2007) Wang et al. (2007) Wang et al. (2007) Kasturirangan (1996) Cao et al. (2006)
Huete et al. (1994)
Reference
Notes: NIR, near-infrared spectral band; R, red spectral band; B, blue band; G, green spectral band. In EVI equation, C 1 = 6, C 2 = 7.5, L = 1; in SAVI and SATVI equations, L = 0.5; in GEMI equation, η = (2(NIR2 − R2 ) + 1.5NIR + 0.5R)/(NIR + R + 0.5); in SATVI equation, SWIR1 is short-wave infrared (band 6 of MODIS), SWIR2 is mid-infrared (band 7 of MODIS).
RVI GRVI GEMI
MSAVI OSAVI
Modified soil-adjusted vegetation index Optimized soil-adjusted vegetation index SATVI
SAVI
Soil-adjusted vegetation index
Soil-adjusted total vegetation index
NDVI GNDVI BNDVI GRNDVI GBNDVI RBNDVI PNDVI TVI IPVI
Normalized difference vegetation index Green NDVI Blue NDVI Green-Red NDVI Green-Blue NDVI Red-Blue NDVI Pan NDVI Transformed difference vegetation index Infrared percentage vegetation index
Ratio vegetation index Green ratio vegetation index Global environment monitoring index
EVI
Abbreviation
Enhanced vegetation index
Name
Table 1. Seventeen vegetation indices and equations.
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in the areas of homogeneous grassland coverage. In our study, 1897 daily MODIS surface reflectance images were chosen to calculate 17 VIs (table 1). To decrease the effects of persistent high cloud coverage within MODIS images for this region, the maximum value composite (MVC) approach used in AVHRR data products (Holben 1986) was employed to composite MODIS VI images. A 10-day MVC image value in terms of sampling date for each site was calculated using equation (1) to minimize the atmospheric effects and sun-surface sensor angular effect on the values of VI image, based on the VI equation (table 1):
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VI = max ((VI)i , (VI)i+1 , (VI)i+2 , (VI)i+3 , . . . , (VI)i+9 ) ,
(1)
where i is the day (e.g. the first day) in which the field survey was conducted for a subplot, i+1 represents the day before, i+2 is the day before that, etc. VI is the vegetation index of the day in which the field survey was conducted. Correlations between the VI values calculated by the 10-day MVC approach and the corresponding forage dry biomass and cover measured in the sampling sites were analysed to select the most suitable VI for monitoring grasslands in the study area. 2.5 Grassland dry biomass, cover monitoring models and composition Linear regression analysis between the field survey data and MODIS data determined the most suitable VI for monitoring the Gannan grasslands. Simulation models of forage dry biomass and cover with the most suitable VI were established by using linear, power, exponential, logarithmic and growth regression methods using the software package SPSS V11.0 (SPSS, Inc., Chicago, IL, USA). Model performance was assessed by the leave-one-out cross-validation (LOOCV) analysis approach (Stone 1974), which was applied to obtain an unbiased estimate of prediction error. In the LOOCV method, all data items, except 1, are iteratively used to build the predictive equation. A single one is held out during each iteration and is then used as validation data (Reeves et al. 2006). The standard procedure for LOOCV involves the use of a single observation from the original sample as the validation data and the remaining observations as the training data. This is repeated such that each observation in the sample is used once as the validation data. The root mean square error of prediction (RMSEP) and the correlation between the observed and predicted response (crossvalidated r) were used to determine the predictive ability of each model. The RMSEP is calculated as:
RMSEP =
n (E(yi ) − yi )2 i=1 n
,
(2)
where E(yi ) is the observed grassland biomass or cover of observation i, yi is the predicted grassland biomass and cover of observation i and n is the total number of observations. The lower the RMSEPs are, the more precise the model, and models returning correlations closer to 1 are more accurate than those returning lower values (Davidson et al. 2006). The LOOCV analysis was performed using MATLAB 7.9 (Mathworks, Inc., Natick, MA, USA). The precision of each model was estimated to select the best model for simulating forage dry biomass and cover in the study area. The annual maximum value of the selected VI in the growing seasons from 2001 to
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2009 was calculated. Then, finally, the forage dry biomass and the cover per pixel were estimated by using the selected models and annual maximum VI images. The annual maximum value of forage dry biomass and cover were calculated using the ArcGIS software package. 2.6 Determining carrying capacity In China, one sheep with 40 kg live weight has been defined as 1 sheep unit (Guo et al. 2004). The number of sheep units per hectare was used to indicate the productivity of grasslands, and it can be estimated using equation (3):
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PA =
Y ×U , I ×D
(3)
where PA is the number of sheep units in one unit of grassland area (sheep unit ha−1 ) during the grazing period; Y is the maximum dry biomass in a grazing season for each pixel (kg DW ha−1 ), which can be estimated with the selected model; U is the usable percentage of grasslands (here U = 50%) (Wang et al. 2006); I is the daily intake of grazing livestock and is 1.8 kg of dried forage per sheep unit; and D is the grazing period in days (here D = 185) (Ren 1998). The grassland carrying capacity (CC) (sheep unit) of each pixel was determined by equation (4): CC = PA × A,
(4)
where A is the area of each pixel (here A = 25 ha). 2.7 Calculation of ecological services value Based on the value of the ecological services of the ecosystem (Costanza et al. 1997), Xie et al. (2001) calculated the ecological services value for each grassland type per unit in China, considering 17 main functions of each grassland type: gas regulation, climate regulation, disturbance regulation, water regulation, water supply, erosion control and sediment retention, soil formation, nutrient cycling, waste treatment, pollination, biological control, refugia, food production, raw materials, genetic resources, recreation and culture. Xie et al. (2001) also calculated the ecological services value in the Qinghai–Tibet Plateau to be US$212.80 per hectare per year (US$ (ha a)−1 ). In Gannan, there are seven types of grasslands, and the condition varies for each grassland type. Vegetation cover is an important parameter, as it affects the capacity of water conservation and soil erosion of grasslands (Guo et al. 2006) and affects the ecological services value of grasslands. Therefore, the vegetation cover of each pixel was used to rectify the ecological services value of grasslands per unit by equation (5): P =
c C
P,
(5)
where c is the vegetation cover of each pixel, which is estimated by the selected model; C is the mean vegetation cover of all pixels of grassland; P is the ecological services value of each pixel with US$212.80 per hectare per year (US$ (ha a)−1 ) (Xie et al. 2001); and P is the corrected ecological services value of each pixel.
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The ecological services value per pixel was defined by equation (6): V = P × A,
(6) −1
where V is the ecological services value of each pixel (US$ a ) and A is the area of each pixel (here A = 25 ha). 2.8 Determining the index of classification management The ICG was defined by equation (7) (Guo et al. 2006):
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ICG =
40 × CC , 40 × CC + V
(7)
where CC is the annual carrying capacity per pixel (equation (4)) and V is the annual ecological services value per pixel. The pixel should be placed in the intensively productive sector when the ICG was more than 0.75 and into the conservation sector when the ICG was below 0.25. Grasslands with an ICG between 0.25 and 0.75 should be placed in the moderately productive sector (Guo et al. 2006). The ICG of each pixel was calculated and classified into the corresponding division sector according to the workflow (figure 2).
Figure 2. Workflow for estimating the ICGs.
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3. Results 3.1 Grassland dry biomass model and accuracy
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From the field survey plots, forage dry biomass varied from 192 to 3525.2 kg DW ha−1 , with an average dry biomass of 1822.61 kg DW ha−1 in the Gannan region, indicating that the productivity of grassland over plots was variable. Linear regression analysis showed that the forage dry biomass had the strongest correlation with the enhanced vegetation index (EVI) with R2 of 0.613 (table 2).
(8) y = 4197.765x − 740.715 R2 = 0.613, P < 0.001 , where y is the dry biomass of grasslands (kg DW ha−1 , x is the EVI) in equation (8), implying that the EVI was the most suitable VI to estimate the forage dry biomass in our study region. Power, exponential, logarithmic and growth regressions were used to simulate the coefficients between the forage dry biomass and the EVI, and the coefficients were different among four equations (table 3). In the five models (table 3), the power model has the lowest RMSEP (426.640), but also has the highest R2 (0.626) and cross-validated r (0.784). Considering these conditions, the power model performed better than the other models (equation (9)) (table 3).
y = 3738.073x1.553 R2 = 0.626, P < 0.001 .
(9)
Table 2. Coefficient of the linear regression between 17 vegetation indices and the forage dry biomass and cover. Vegetation indices
Biomass R2
Cover R2
Vegetation Biomass indices R2
Cover R2
Vegetation Biomass Cover indices R2 R2
EVI NDVI GNDVI BNDVI GRNDVI GBNDVI
0.613 0.450 0.358 0.307 0.424 0.352
0.444 0.384 0.309 0.276 0.344 0.292
RBNDVI PNDVI TVI IPVI SAVI MSAVI
0.333 0.318 0.396 0.384 0.425 0.411
OSAVI SATVI RVI GRVI GEMI
0.409 0.404 0.439 0.450 0.561 0.562
0.554 0.266 0.436 0.366 0.549
0.443 0.286 0.250 0.243 0.412
Table 3. Regression models between forage dry biomass (kg DW ha−1 ) and EVI (P < 0.001, N = 228). Model Linear Power Exponential Growth Logarithmic
Model equation
R2
RMSEP
Cross-validated r
y = 4197.765x – 740.715 y = 3738.073x1.553 y = 309.880e2.761x y = e(5.736 + 2.761x) y = 3013.714 + 2301.169 ln(x)
0.613 0.626 0.613 0.613 0.595
426.973 426.640 432.434 432.434 427.117
0.783 0.784 0.778 0.778 0.784
Note: N, number of samples.
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3.2 Grassland cover model and accuracy Field surveys showed that grassland cover varied from 7% to 100%, with an average of 88.72%. Based on the results from the linear regression analysis, the grassland cover had the strongest correlation with the EVI (table 2):
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y = 72.186x + 44.645 R2 = 0.444, P < 0.001 ,
(10)
where y is the grassland cover (%) and x is the EVI. The EVI was used to estimate the grassland cover in this study. The outcomes applying power, exponential, logarithmic and growth regressions between the grassland cover and the EVI are listed in table 2. The results of cross-validation for the regression models showed that the correlations of the power, exponential and growth models with the EVI are not significant, and the logarithmic model is used to estimate grassland cover, considering that it has the highest R2 (0.449) and cross-validated r (0.683) lower RMSEP (11.97) (equation (11)) (table 4):
y = 101.664 + 42.386 ln (x) R2 = 0.449, P < 0.001 ,
(11)
where y is the grassland cover (%) and x is the EVI, which should be used to estimate the grassland cover in this study. 3.3 Classification of grassland management Spatial distributions of grassland dry biomass and cover vary annually due to climatic elements and anthropogenic factors (figures 3 and 4). Forage dry biomass was estimated using the power model, and the carrying capacity was also calculated to assess the economic value. The logarithmic model estimated the grassland cover, and this in turn corrected the ecological services value based on the mean ecological services value per unit in the study region. The value of the ICG was calculated by equation (7). This study indicated that the value of ICG at the pixel scale varied from 0.073 to 0.605 during 2001–2009 and was smaller than 0.75, implying that no grasslands could be used to conduct intensive animal production at the pixel scale. According to the ICG, grasslands in the study region were divided into moderately productive and conservation regions, but the intensively productive grassland sector was not present in the region from 2001 to 2009. The area of conservation and moderately productive sectors varied slightly from 2001 to 2009 (figure 5), and this was not only related to annual rainfall but also to grassland management measures (such as Table 4. Regression models between grassland cover (%) and EVI (P < 0.001, N = 228). Model Linear Power Exponential Growth Logarithmic
Model equation
R2
RMSEP
Cross-validated r
y = 72.186x + 44.645 y = 121.710x0.653 y = 44.633e1.089x y = e(3.798 + 1.089x) y = 101.664 + 42.386 ln(x)
0.444 0.354 0.304 0.304 0.449
11.302 12.204 12.796 12.796 11.970
0.636 0.663 0.628 0.628 0.683
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Figure 3. Grassland dry biomass (kg DW ha−1 ) map from 2001 to 2009 in the Gannan region.
grazing intensity or fencing). As a means to manage the grasslands, the average forage biomass and cover were used to calculate the area of conservation and moderately productive sectors from 2001 to 2009, and these areas were considered as standard management grasslands in the next 8 years (figure 6), in which the moderately productive grasslands were 250.44 × 104 ha with 97.96% of the total available grasslands, and the conservation grasslands accounted for 2.04% of the total available grasslands with 5.22 × 104 ha. 4. Discussion 4.1 Establishment of forage dry biomass and cover models The VIs (table 1), except for the EVI, correct for either soil or atmospheric effects, but fail to eliminate both of these effects simultaneously (Huete 1988, Rondeaux et al. 1996, Steven 1998, Xiao et al. 2003). The VI algorithms of the green normalized difference vegetation index (GNDVI), blue NDVI (BNDVI), green-red NDVI (GRNDVI), green-blue NDVI (GBNDVI), red-blue NDVI (RBNDVI), Pan NDVI (PNDVI), transformed difference vegetation index (TVI) and infrared percentage vegetation index (IPVI) are similar to NDVI, as they are defined as the difference between near-infrared (NIR) and visible spectral reflection divided by their sum; the algorithms of the green ratio vegetation index (GRVI) and ratio vegetation index (RVI) are NIR/green and NIR/red, respectively. All the VIs mentioned above are
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Figure 5. Changes of the conservation sector and moderately productive sector areas from 2001 to 2009 in the Gannan region.
sensitive to soil and atmospheric effects. There is a soil-adjusted factor in the algorithms of the soil-adjusted vegetation index (SAVI), modified SAVI (MSAVI) and optimized SAVI (OSAVI), which can minimize the effect of the soil background
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Figure 6. Average areas of the conservation sector and moderately productive sector during 2001–2009 in the Gannan region.
but is affected by the aerosols in the atmosphere. Although a soil-adjusted factor is considered in the VI of the soil-adjusted total vegetation index (SATVI), it is only suitable to monitor the grasslands where forbs comprised more than 30% (Marsett et al. 2006). The global environment monitoring index (GEMI) is designed specifically to reduce the relative effects of atmospheric perturbations, while maintaining the information about the vegetation (Pinty and Verstraete 1992). The GEMI is less sensitive to atmospheric effects, but incapable of dealing with variations in soil reflectance. The NDVI is chlorophyll sensitive, and the EVI is more responsive to canopy structural variations, including leaf area index (LAI), canopy type, plant physiognomy and canopy architecture (Gao et al. 2000, Huete et al. 2002). The EVI can minimize the effects of atmosphere and canopy background that contaminate the NDVI (Huete et al. 1997) and enhance the green vegetation signal with improved sensitivity in high-biomass regions and improved vegetation monitoring through a de-coupling of the canopy background signal and a reduction in atmosphere influences (Huete et al. 2002, Xiao et al. 2003). Therefore, the EVI is insensitive not only to soil background but also to atmospheric effects through the introduction of the atmospheric information contained in the blue channel. The EVI might be more sensitive to alpine grassland vegetation biomass/cover, especially in the grow peaks for alpine shrub grassland and alpine meadow in the Tibetan plateau (Li et al. 2007, Yang et al. 2009, Fu et al. 2010, Mi et al. 2010, Wang et al. 2010). The power and logarithmic models of the EVI were chosen to estimate the grassland dry biomass and cover, respectively, in this study. Remotely sensed VIs have
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been successfully applied to estimate forage biomass and cover in the Inner Mongolia region of China (Xu et al. 2007), North America (Sims et al. 2008), Jordan (Ai-Bakri and Taylor 2003) and savanna grasslands of Africa (Verbesselt et al. 2006). Although many VIs can be used to monitor and evaluate the forage biomass and cover, this study shows that the correlation between 17 VIs and forage dry biomass and cover is different and that the EVI is the most suitable VI for monitoring Gannan grassland, supporting the notion that the selection of VIs for monitoring grasslands ought to be conducted with care. In the study region, the NDVI did not rate as highly for estimating forage dry biomass and cover; this does not agree with research conducted in the south-west of Western Australia (Hill et al. 2004) and Inner Mongolia, China (Xie et al. 2009), in which the NDVI has been used to estimate pasture growth rate and aboveground grassland biomass. However, the results of this study concur with similar studies conducted in the Tibetan plateau (Li et al. 2007, Yang et al. 2009, Fu et al. 2010, Mi et al. 2010, Wang et al. 2010) and Colombia (Anaya et al. 2009), in which the EVI is used to estimate the plant biomass. Research shows that the sensitivity of the NDVI rapidly decreases when vegetation cover is greater than 80% (Wen and Wang 1997), and the work presented here supports the view that the EVI has a stronger relationship than other VIs in the study region with more than 80% grassland cover. The EVI is derived from the NDVI with one additional spectral band and is considered to optimize the vegetation signal and reduce atmospheric aerosol influences. This study further confirms that the EVI is ideal for estimating the biomass and cover in the higher grassland cover regions of China (Yang et al. 2009). Additionally, this study shows that the power model and logarithmic model are suitable for estimating forage dry biomass and cover. This differs from the results reported by Anaya et al. (2009) and Yang et al. (2009), in which a linear model has been suggested to estimate the plant biomass in Tibet and Colombia. The contrasts between this research and that which indicates that the linear model works best for plant biomass estimation is likely to be due to the need to utilize different models dependent upon the ecological characteristics of the region under study regardless of the VI used to characterize the biomass. The analysis in this study is limited by the use of MODIS data, as spectral mixture problems exist due to an unavoidable mismatch between the scale of the MODIS imagery (one pixel in size is 500 m × 500 m) and that of the field data (10 m × 10 m for the plots and 1 m × 1 m for the subplots). The information contained within the subplot data represents only a small fraction of a MODIS pixel, and although an attempt to control for this was done by ensuring that the surrounding landscape (e.g. surrounding the plots) did not vary greatly from that within the plots, variation within what is captured per MODIS pixel is inevitable and remains a limitation of this research. The grassland biomass and cover models were simulated using field survey data and corresponding to the EVI values mainly from July to August. However, the EVI values are only between 0.3 and 0.9 during the specific time, which might lead to uncertainty and errors for grassland biomass and cover simulations, which will in turn affect the classification management map. However, this can be helped by improving the precision of grassland biomass and cover simulations. 4.2 Classification management for grassland region This study has demonstrated that the Gannan region can be divided into conservation grasslands and moderately productive grasslands, similarly to the grassland
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division in the Aletai northern region of China (Guo et al. 2006), but does not conform to the grassland division in Gansu Province (Guo et al. 2003). Grasslands in the Gannan region and Aletai region can be divided into two regions as the superior grasslands have been converted into crops (wheat, highland barley and rape, etc.) (Yue and Wei 2009), and present grasslands are distributed in the relatively harsh environments, which are less likely to be used for intensive production due to low temperature (Wheeler et al. 2000). Grasslands in Gansu Province are divided into three sectors, as the flat meadowland can be used for intensive management due to higher temperatures, available irrigation and increased sunshine hours (Chartzoulakis and Psarras 2005). The area of each management sector in this pattern is closely related to regional economic development and social demand for grasslands. The area of the conservation sector in the Gannan region comprises 2.04% of total available grasslands and is lower than that in the Aletai region with 25.4% and in Gansu with 38%, and the area of the moderate production sector in this study area accounts for 97.96% and is higher than that in the Aletai region with 74.6% (Guo et al. 2006) and in Gansu with 55% (Guo et al. 2003). The net income per capita of the Aletai region and the Gansu Province is about 4 and 2.5 times, respectively, of those of the Gannan region (Fan 2006, Xinjiang Uighur Autonomous Region Statistical Bureau 2006); this enables the residents in the Aletai region and Gansu Province to concentrate on the ecological management of grassland ecosystems to a greater extent than the residents in the Gannan region. The area of the conservation sector in the Gannan region will increase in the future. This change will be due to the increase in net income per capita with economic development and with financial compensation provided by the government. In 2003, the Chinese government introduced an anti-grazing programme on the grasslands in the Gannan region by implementing an ecological compensation policy. However, this programme has not altered the area of the conservation sector because current debates are still addressing which parts of the grasslands are to be conserved. Until there is consensus and government support of the classification management map, this management strategy will not be implemented. The increase in the extent of the conservation sector depends on an increase in the threshold of the ICG. The ICG threshold will be varied in practice as classification management for grasslands will be used in different regions with different demands for grasslands and economic development. This study establishes a management classification procedure for grasslands using MODIS information, which improves upon the previous procedure proposed by Guo et al. (2004), and allows for an assessment of the management classification over a wide spatial extent, thereby offering a standardized method for developing a management classification that can be used across various areas with different ecological and economic demands. Different management techniques should be applied to conservation, moderately and intensive productive grasslands. Although the grasslands in a conservation sector cannot provide an income for resident farmers, these losses can be compensated by the increased income of intensively productive grasslands. In this study, grasslands divided into conservation sectors are severely degraded and need to be conserved, and these grasslands can be mainly devoted to ecological and social values in the future. Anti-grazing and fencing have been suggested to restore degraded grasslands in the Gannan region, as they have been successfully utilized in the Europe and many other parts of the world (Kleijn and Sutherland 2003, Gibon 2005, Carvalho and Batello 2009). It is suggested that moderately productive grasslands have reasonable carrying capacity and rotational grazing by grazing in the summer and autumn and shelter
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feeding in winter and spring to achieve a balance between livestock need and forage supply. These policies have been adopted in many other parts of the world, such as the eastern Amazon (Hohnwald et al. 2006) and The Netherlands (Bakker and Ter Heerdt 2005). Classification management for grassland is an open pattern; each management sector is not fixed and can be redivided based on economic development and the changes of social demand. To conveniently manage grasslands, management measures need to be kept steady in a given time; this period depends on the social demand and the condition of grasslands. This study suggests the average area of conservation grasslands and moderately productive grasslands from 2001 to 2009 is considered to manage sectors in the next 8 years (figure 6). Acknowledgements This study was supported in part by the Social Science Foundation of China (08BJY035), the Cultivation Fund of the Key Scientific and Technical Innovation Project, Ministry of Education of China (#708089) and the ‘863’ Project of China (2007AA10Z232). We are grateful to Dr Cerian Gibbes and Dr Pinki Mondal for their fruitful suggestions in improving the language of the article. The authors thank the two anonymous reviewers for their very helpful comments and suggestions that substantially improved the manuscript. References AI-BAKRI, J.T. and TAYLOR, J.C., 2003, Application of NOAA AVHRR for monitoring vegetation conditions and biomass in Jordan. Journal of Arid Environments, 54, pp. 579–593. AN, H.Y., YAO, Y.B., YIN, D., WANG, R.Y., CHENG, C.P. and ZHANG, X.Y., 2007, Ecoclimate resources and ecological agriculture division in Gannan plateau. Arid Meteorology, 25, pp. 67–72. ANAYA, J.A., CHUVI, E. and PALACIOS-ORUETA, A., 2009, Aboveground biomass assessment in Colombia: a remote sensing approach. Forest Ecology and Management, 257, pp. 1237–1246. ASRAR, G., WEISER, R.L., JOHNSON, D.E. and KILLEEN, J.M., 1986, Distinguishing among tall grass prairie cover types from measurements of multispectral reflectance. Remote Sensing of Environment, 19, pp. 159–169. BAKKER, J.P. and TER HEERDT, G.N.J., 2005, Organic grassland farming in the Netherlands: a case study of effects on vegetation dynamics. Basic and Applied Ecology, 6, pp. 205–214. BAUMER, M., 1982, Grassland management and the environment. Journal of Rangeland Management, 35, pp. 3–4. BRAGG, O.M. and TALLIS, J.H., 2001, The sensitivity of pest-covered upland landscapes. Catena, 42, pp. 345–360. CAO, Y., CHEN, H., OUYANG, H. and XIAO, D.N., 2006, Based on remote sensing data: a case study of Ejin natural oasis landscape. Journal of Natural Resources, 21, pp. 481–488. CARVALHO, P.C.F. and BATELLO, C., 2009, Access to land, livestock production and ecosystem conservation in the Brazilian Campos biome: the natural grassland dilemma. Livestock Science, 120, pp. 158–162. CHARTZOULAKIS, K. and PSARRAS, G., 2005, Global change effects on crop photosynthesis and production in Mediterranean: the case of Crete, Greece. Agriculture, Ecosystems and Environment, 106, pp. 147–157. COSTANZA, R., D’ARGE, R., DE GROOT, R., FARBER, S., GRASSO, M., HANNON, B., LIMBURG, K., NAEEM, S., O’NEILL, R.V., PARUELO, J., RASKIN, R.G., SUTTON, P. and VAN DEN
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